This disclosure relates to information management.
Generative models and algorithms that act upon generative models are tools of machine learning. A generative model defines a number of variables and describes relationships among the variables. Often, an algorithm that acts upon a generative model endeavors to compute probabilistic information about the values of certain variables given values of other variables.
Generative models can be used to model textual documents in a way that allows an algorithm to provide an underlying meaning for a given piece of text, i.e., the text's semantics. For example, generative models can be used to analyze an original document's semantics to find other documents having similar content to the original document. Similarly, in the context of search engines, a search query entered by a user can be matched to documents in a repository based on the underlying meaning of the search query.
Textual systems based on generative models can learn the generative model (variables and relationships) by, for example, examining many pieces of text from training data. In the context of very large generative models, having many variables and much training data (e.g., millions or billions of text pieces), difficulty may arise in determining whether one generative model is better than another generative model at describing relationships and facilitating algorithms.